Evidence map›Paper›PMID 41166991›Full record

ArticleEBioMedicine2025

AI-derived body composition analysis reveals muscle volume and metformin-associated adipose effects and the obesity paradox in non-small cell lung cancer.

Xinan Wang, Akinori Hata, Noriaki Wada, Jiyeon Song, Taiki Fukuda, Yusei Nakamura, Mizuki Nishino, Yi Li, Mark L Schiebler, David C Christiani and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xinan WangDepartment of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA. Electronic address: xinanwang@hsph.harvard.edu.
Akinori HataDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Diagnostic and Interventional Radiology, Osaka University Graduate School of Medicine, Osaka, Japan.
Noriaki WadaDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Jiyeon SongDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, USA.
Taiki FukudaDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Yusei NakamuraDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Mizuki NishinoDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Yi LiDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, USA.
Mark L SchieblerDepartment of Radiology, UW Madison School of Medicine and Public Health, Madison, WI, USA.
David C ChristianiDepartment of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Hiroto HatabuDepartment of Radiology, Center for Pulmonary Functional Imaging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Funding

The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
New Statistical Methods for Modelling Cancer OutcomesR01CA249096 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Yi Li · 2021 to 2026
$2.5M
Detecting racial disparities in cancer survival by integrating multiple high-dimensional observational studiesR01CA269398 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GUHA, SUBHARUP, LI, YI · 2022 to 2025
$1.3M
Human leukocyte antigen and immune response in non-small cell lung cancer: A multi-omics approachK99CA297010 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Xinan Wang · 2025 to 2026
$248k
NCI NIH HHS K99 CA297010NCI NIH HHS R01 CA249096NCI NIH HHS R01 CA269398NCI NIH HHS R35 CA197449NCI NIH HHS U01 CA209414
6 · The paper itself

Abstract

backgroundBody composition is emerging as a prognostic factor in cancer, with metformin showing potential antitumour effects in patients with obesity and non-small cell lung cancer (NSCLC). We aimed to determine which specific body composition components drive survival differences in different patient subgroups and explore underlying molecular mechanisms.

methodsIn this retrospective cohort study, we analysed 1275 patients with confirmed NSCLC diagnosis and available chest CT imaging for body composition analysis at the Massachusetts General Hospital. Six body composition parameters (muscle density and volume, visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], VAT/SAT ratio, and intramuscular adipose tissue [IMAT]) were quantified using validated CNN-based segmentation algorithms from baseline CT scans. The primary outcome was overall survival (OS), assessed from diagnosis until death or last follow-up. Associations with OS were analysed using multivariable Cox proportional hazards model, followed by subgroup analyses by sex, BMI, diabetes, and metformin use. Circulating cytokine levels (n = 93) and oncogenic driver mutations (n = 646) from the imaging cohort and comprehensive tumour mutation profiles from an independent Memorial Sloan Kettering Cancer Center cohort (n = 5363) were analysed for mechanistic insights.

findingsMuscle volume demonstrated a dose-response relationship with OS (T2 vs. T1: HR = 0.78, 95% CI: 0.65-0.94; T3 vs. T1: HR = 0.71, 95% CI: 0.57-0.88) and the effect was intensified in patients with a BMI ≥25 kg/m

interpretationHigher muscle volume consistently predicts improved survival in patients with NSCLC, with enhanced effects in patients with elevated BMI or diabetes. Increased adipose tissue components benefit only metformin users, suggesting drug-specific metabolic interactions. Our work demonstrates the clinical utility of detailed body composition and suggests opportunities for personalised treatment approaches based on comprehensive body composition profiles rather than BMI alone.

fundingThis work was supported by grants from the National Cancer Institute of the National Institutes of Health: U01CA209414 to X.W. and D.C.C., 1K99CA297010 to X.W.

Indexed as

Adipose TissueBody CompositionCarcinoma, Non-Small-Cell LungLung NeoplasmsMetforminObesityAgedFemaleHumansHypoglycemic AgentsMaleMiddle AgedMuscle, SkeletalObesity ParadoxPrognosisRetrospective StudiesHypoglycemic AgentsMetforminBiomarkerBody compositionLung cancerMetforminObesitySurvival

Identifiers

PMID41166991
PMCPMC12613069

What Socratic holds

Texttitle and abstract
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.